Chromatin Architecture as an Essential Determinant of Dendritic Cell Function
Bibliographic record
Abstract
Epigenetics has widespread implications in a variety of cellular processes ranging from cell identity and specification, to cellular adaptation to environmental stimuli. While typically associated with heritable changes in gene expression, epigenetic mechanisms are now appreciated to regulate dynamic changes in gene expression-even in post-mitotic cells. Cells of the innate immune system, including dendritic cells (DC), rapidly integrate signals from their microenvironment and respond accordingly, undergoing massive changes in transcriptional programming. This dynamic transcriptional reprogramming relies on epigenetic changes mediated by numerous enzymes and their substrates. This review highlights our current understanding of epigenetic regulation of DC function. Epigenetic mechanisms contribute to the maintenance of the steady state and are important for precise responses to proinflammatory stimuli. Interdependence between epigenetic modifications and the delicate balance of metabolites present another layer of complexity. In addition, dynamic regulation of the expression of proteins that modify chromatin architecture in DCs significantly impacts DC function. Environmental factors, including inflammation, aging, chemicals, nutrients, and lipid mediators, are increasingly appreciated to affect the epigenome in DCs, and, in doing so, regulate host immunity. Our understanding of how epigenetic mechanisms regulate DC function is in its infancy, and it must be expanded in order to discern the mechanisms underlying the balance between health and disease states.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".